Prevalence and outcomes of Clostridioides difficile infection in liver transplant recipients. Issue 1 (7th December 2021) - Kortt, Nicholas C.; Santhakumar, Cositha; Davis, Rebecca J.; Strasser, Simone I.; McCaughan, Geoffrey W.; Liu, Ken; Majumdar, Avik
Abstract: Background and aim: Data are limited on whether Clostridioides difficile infection (CDI) in the first year after liver transplantation (LT) is associated with increased mortality. In an Australian setting without hypervirulent strain of C. difficile we investigated the prevalence, risk factors, and patient survival associated with CDI in LT. Methods: Consecutive patients who underwent deceased‐donor LT from 2007 to 2017 were studied retrospectively. Prevalence and long‐term outcomes of LT recipients with and without CDI were examined in the entire LT cohort. A case–control study was performed to investigate risk factors associated with CDI. Results: Six hundred and forty‐nine patients underwent LT, of which 32 (4.9%) were diagnosed with CDI within the first 12 months post‐LT. There was no difference in patient survival in the overall LT cohort on Kaplan–Meier analysis when stratified by CDI status (log‐rank test, p  = .08). Furthermore, age was the only predictor of mortality on Cox regression (hazard ratio (HR) 1.06, 95% confidence interval (CI) 1.00–1.13, p  = .03). On multivariable logistic regression, rifaximin pre‐LT reduced risk (odds ratio (OR) 0.22, 95% CI 0.65–0.74, p  = .01) whereas antibiotics pre‐LT (OR 7.02, 95% CI 1.26–39.01, p  = .03) and length of hospital stay after LT (OR 1.03, 95% CI 1.01–1.06, p  = .02) were associated with increased risk of CDI. Conclusions: Within the local setting of our study, CDI within 12 months post‐LT is of low severity, associated with pre‐LT antibiotic exposure and longer hospital stay but no survival impact after LT. Rifaximin use pre‐LT reduced the risk of CDI post‐LT. Abstract : Predicting the arrival aircraft's flight time plays a critical role in effectively optimizing and scheduling spatial‐temporal resources in the terminal airspace. This paper focuses on a data‐driven method for predicting the arrival flight time by proposing new methods for constructing features.